Skip to main content
Glama

mcp_opendaw_match_to_reference

Match your mix to a reference track by adjusting loudness, spectral balance, and stereo width. Outputs a corrected WAV file with analysis of applied changes.

Instructions

Automatically match your mix to a reference track — spectral + loudness alignment.

Like Phantom's match_to_reference: takes your mix and a reference, then:

  1. Measures LUFS difference → applies gain compensation

  2. Measures per-band spectral difference → applies EQ correction

  3. (Optional) Measures stereo width → applies stereo adjustment

Outputs a matched WAV file. This is automated A/B matching — the mix gets as close to the reference as possible without re-mixing.

filename: Your mix WAV (exports dir or absolute path). reference: Reference track WAV (exports dir or absolute path). output_filename: Output filename (default: _matched.wav). match_lufs: Match integrated LUFS. match_spectrum: Match per-band spectral energy (7-band EQ correction). match_stereo: Match stereo width (experimental).

Returns analysis of what was applied + output file path.

Example: match_to_reference("my_mix.wav", "pro_track.wav")

→ {lufs_adjusted: +1.4 dB, eq_curves: [...], output: "my_mix_matched.wav"}

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
filenameYes
referenceYes
match_lufsNo
match_stereoNo
match_spectrumNo
output_filenameNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries full burden. It details the automated process: gain compensation, EQ correction, optional stereo adjustment, output of a matched WAV file, and a return object with analysis. It labels stereo matching as 'experimental'. While it doesn't mention file permissions or side effects, it adequately discloses the main behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured: a concise purpose sentence, step-by-step breakdown, parameter list, and example. Each section adds value without redundancy. The parameter list is clear and the example illustrates typical usage. No unnecessary information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description covers the tool's purpose, operation steps, parameter details, and return structure (via example). Given that the tool has an output schema, the example return is a bonus. For a moderately complex matching tool, the description is sufficiently complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0% (no parameter descriptions in schema). The description compensates by listing all 6 parameters with clear explanations: filename, reference, output_filename, and three boolean flags (match_lufs, match_spectrum, match_stereo) with defaults. An example call further clarifies usage. This fully compensates for the missing schema descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's function: automatically matching a mix to a reference track via spectral and loudness alignment. It explicitly references 'Phantom's match_to_reference' and explains the three-step process, making the purpose unmistakable and distinguishing it from sibling tools like compare_to_reference.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explains the tool's workflow (LUFS, spectral, stereo matching) and provides an example. It implies usage for A/B matching but does not explicitly state when not to use it or suggest alternatives. The reference to a known plugin helps, but lacks direct exclusions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/AMEOBIUS-team/opendaw-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server